Sr Lead AI Platform Engineer

Overview

In this Senior VP role, you own and evolve the platform, tooling, and infrastructure behind agentic AI and ML products within IPB AIML. You set standards for deployment, scalability, security, and reliability as production platforms multiply. You collaborate with data and AI engineers to productionize models and reduce operational risk, while mentoring engineers and advancing a culture of inclusion. This is a hands-on, high-impact position at a globally influential financial services firm with a strong focus on scalable, secure AI platforms.

Responsibilities

• Design and build the team’s platform, including deployment pipelines, model serving, containerisation, orchestration, and environment management
• Define reliability, observability, and operational excellence for production AI/ML services
• Develop tooling and paved paths for safe and fast AI/ML product delivery
• Implement platform-level security, secrets management, and access controls to firm-wide standards
• Collaborate with data/AI engineers to productionize models and coordinate with infra teams to reduce risk
• Lead capacity, cost, and performance management for compute and inference workloads
• Mentor platform and DevOps engineers and establish engineering standards via design/code reviews
• Champion diversity, inclusion, and respect within the team

Key requirements

• 5+ years of applied software engineering experience
• Advanced proficiency in Python and infrastructure-as-code
• Deep hands-on experience with Kubernetes, containerisation, and cloud-native deployment patterns
• Strong CI/CD experience with deployment and release automation
• Experience serving, scaling, and monitoring ML models or data-intensive services (MLOps)
• Practical experience with observability tooling (metrics, logging, tracing) and incident response
• Experience operating ML/LLM workloads in production (LLMOps, cost/perf management)
• Strong communication skills and ability to set standards for others
• Industry-recognised Kubernetes certification (e.g., CKAD or similar)
• Master’s degree in Computer Science, Engineering, or a related technical field (or equivalent)

• strong communication
• leadership
• mentoring
• Python
• infrastructure-as-code
• Kubernetes

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